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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ComTech: Computer, Mathematics and Engineering Applications Jurnal Statistika dan Aplikasinya Advance Sustainable Science, Engineering and Technology (ASSET)
Erfiani
IPB University
Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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Pemodelan Regresi Logisitik Biner pada Faktor-Faktor yang Memengaruhi Indeks Pembangunan Manusia di Pulau Jawa Ilham Azagi; Erfiani; Indahwati; Anwar Fitrianto; Reni Amelia
Jurnal Statistika dan Aplikasinya Vol 6 No 1 (2022): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.06101

Abstract

Indeks Pembangunan Manusia dibangun dari tiga dimensi dasar, dimensi ini merupakan pendekatan yang dipilih dalam penggambaran kualitas hidup manusia dan tidak mengalami perubahan hingga saat ini. Ketiga dimensi ini mencakup umur panjang dan hidup sehat, pengetahuan, dan standar hidup layak Penelitian ini bertujuan untuk menentukan model regresi logistik biner dan faktor-faktor yang mempengaruhi Indeks Pembangunan Manusia di Pulau Jawa menggunakan regresi logistik biner. Regresi logistik biner adalah analisis statistika yang digunakan untuk mengetahui pengaruh suatu satu atau lebih variabel independen terhadap satu variabel dependen, dimana variabel dependen bersifat biner atau dichotomous. Hasil penelitian ini menunjukkan model regresi logistik biner yang terbentuk adalah Y = -111.32025 + 0.58148X1 + 3.07145X2 + 2.40689X3 + 0.79014X4 – 0.87295X5 + 0.02139X6 + 0.11445X7. Faktor-faktor yang berpengaruh terhadap kategori pada nilai IPM terhadap variabel bebas dengan taraf nyata α = 5% adalah Persentase umur harapan hidup (X1), Persentase harapan lama sekolah (X2), dan Persentase rata-rata lama sekolah (X3). Evaluasi model diperoleh nilai balanced accuracy sebesar 96.43%, yang berarti model baik digunakan.
Dynamic Time Warping Techniques for Time Series Clustering of Covid-19 Cases in DKI Jakarta Meicheil Yohansa; Khairil Anwar Notodiputro; Erfiani
ComTech: Computer, Mathematics and Engineering Applications Vol. 13 No. 2 (2022): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v13i2.7413

Abstract

The number of positive cases of Covid-19 in DKI Jakarta has contributed to the national issues, reaching 25% of the total cases in Indonesia. The research examined and modeled the distribution pattern of Covid-19 positive cases in DKI Jakarta based on 44 districts spreading over six administrative areas. The data were regarding positive Covid-19 cases in DKI Jakarta for the past year, from April 2020 to April 2021. The research related to the pattern of positive Covid-19 distribution in 44 districts was carried out by time series clustering through Dynamic Time Warping (DTW) distances and agglomerative hierarchical methods. Then, the effectiveness of the clustering process is evaluated by comparing the predicted value of Covid-19 cases between clustering and non-clustering forecast results at the city level for the next 14 days through the Autoregressive Integrated Moving Average (ARIMA) model. The results group 44 districts into 6 optimal clusters based on the pattern of positive cases of Covid-19 in each district. The highest distribution rate is in cluster A, and the lowest is in cluster F. Geographical characteristics are also indicated by clusters A, B, E, and F. Then, the results show that the Mean Average Percentage Error (MAPE) value of the clustering model ranges from 16% to 20%. The difference between MAPE values to the non-clustering model implies that the forecasting accuracy is not far apart, which is in the round of 5%−6%.
Pemodelan Regresi Logistik Berbasis Backward Elimination Untuk Mengetahui Faktor yang Mempengaruhi Tingkat Kemiskinan di Indonesia Tahun 2021 Alfi Indah Nurrizqi; Erfiani; Indahwati; Anwar Fitrianto; Reni Amelia
Jurnal Statistika dan Aplikasinya Vol 6 No 2 (2022): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.06202

Abstract

Kemiskinan seringkali berhubungan dengan masalah kesejahteraan dan menjadi salah satu masalah utama di Indonesia. Kondisi ekonomi akibat Covid-19 berdampak pada tingkat kemiskinan di masyarakat. Penelitian ini bertujuan untuk mengetahui faktor-faktor yang memengaruhi tingkat kemiskinan di Indonesia tahun 2021 menggunakan regresi logistik biner. Regresi logistik biner digunakan untuk memodelkan hubungan antara variabel respon yang terdiri dari dua kategori dengan satu atau lebih variabel prediktor. Hasil penelitian ini menunjukkan proporsi tingkat kemiskinan rendah lebih tinggi dibandingkan tingkat kemiskinan tinggi. Terdapat 18 provinsi dengan tingkat kemiskinan rendah serta 16 provinsi dengan tingkat kemiskinan tinggi. Faktor-faktor yang berpengaruh terhadap kategori pada tingkat kemiskinan yaitu Indeks Pembangunan Manusia (X1) dan Gini Ratio (X2). Ketepatan klasifikasi dari model sebesar 83.33%, yang artinya model baik digunakan.
Regresi Ordinal Logit dan Probit pada Faktor Kesejahteraan Rumah Tangga Petani Tanaman Pangan di Provinsi Sulawesi Tenggara Titin Yuniarty Yuniarty; Erfiani; Indahwati; Anwar Fitrianto; Khusnia N. K.
Jurnal Statistika dan Aplikasinya Vol 6 No 2 (2022): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.06216

Abstract

Sektor pertanian memiliki sumbangsih besar dalam perekonomian Indonesia, tetapi menjadi sektor yang seringkali diidentikkan dengan kemiskinan. Hingga saat ini, pembangunan sektor pertanian belum mampu sepenuhnya meningkatkan kesejahteraan rumah tangga petani. Provinsi Sulawesi Tenggara selama 2013-2021 tercatat memiliki Indeks Nilai Tukar Petani (NTP) untuk subsektor tanaman pangan, stabil di bawah 100. Indeks NTP di bawah 100 menunjukkan bahwa kesejahteraan petani belum begitu baik. Penelitian ini bertujuan menentukan faktor determinan kesejahteraan rumah tangga petani tanaman pangan di Provinsi Sulawesi Tenggara. Status kesejahteraan merupakan peubah respon berskala ordinal dengan tiga kategori yaitu miskin, rentan miskin, dan tidak miskin. Metode regresi yang sesuai untuk peubah respon berskala ordinal adalah regresi ordinal, dengan beberapa kemungkinan fungsi hubung. Dalam kajian ini menggunakan fungsi hubung logit dan probit. Hasil analisis regresi menunjukkan bahwa umur kepala rumah tangga (X3), kepemilikan telepon seluler (X6), sumber penghasilan utama rumah tangga (X9), akses terhadap kredit usahatani (X11), dan keberadaan jaminan sosial rumah tangga (X13) berpengaruh positif dan signifikan terhadap status kesejahteraan rumah tangga petani tanaman pangan, sedangkan jumlah anggota rumah tangga (X4) dan usia kawin pertama (X5) berpengaruh negatif dan signifikan. Dengan membandingkan nilai R2 dan balanced accuracy model logit dan probit, disimpulkan bahwa model logit lebih baik dalam mengidentifikasi faktor determinan kesejahteraan rumah tangga petani tanaman pangan di Provinsi Sulawesi Tenggara daripada model probit.
Pemodelan Regresi Logistik Ordinal Backward dengan Imputasi K-Nearest Neighbour pada Indeks Pembangunan Manusia di Indonesia Tahun 2021 Muftih Alwi Aliu; Anwar Fitrianto; Erfiani; Indahwati; Khusnia N. K.
Jurnal Statistika dan Aplikasinya Vol 7 No 1 (2023): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07105

Abstract

The human development index (HDI) is one of the important things to note in Indonesia today. The growth of HDI in Indonesia in 2021 is not evenly distributed in all regencies/cities and has high disparities. This study aims to find out the description of HDI data, get the best model to determine the factors that significantly affect the HDI of regencies/cities in Indonesia in 2021 and identify the classification accuracy results of the best model. The independent variables used in this study are average years of schooling, open unemployment rate, population growth rate, population density, percentage of poor people and sex ratio. The independent variables in this study contained missing values, so they were handled using k-nearest neighbour (KNN) imputation and continued modelling using ordinal logistic regression using the backward elimination technique to obtain significant factors. The results showed that the proportion of the low HDI category was 4.28%, the medium HDI category was 48.64%, and the high HDI category was 47.08%. Based on logistic regression modeling using backward elimination which has the smallest AIC value of 293.387, a model with independent variables of average years of schooling (X1), population density (X4), percentage of poor people (X5) and sex ratio (X6) is a variable that significantly affects the HDI of regencies/cities in Indonesia in 2021. The accuracy value of the classification accuracy of training data and test data from the ordinal logistic regression model of HDI of regencies/cities in Indonesia in 2021 is 83.46% and 86.61%, respectively, which means that the model is good for prediction.
Comparative Performance of GLMM and GEE for Longitudinal Beta Regression in Economic Inequality Modelling Pardomuan Robinson Sihombing; Erfiani; Khairil Anwar Notodiputro; Anang Kurnia
Advance Sustainable Science Engineering and Technology Vol. 7 No. 3 (2025): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i3.2057

Abstract

Due to the shortcomings of conventional Gaussian methods, specialized models are frequently needed for longitudinal data analysis with bounded outcomes, such as the Gini ratio. In order to model economic inequality in Indonesia, this study compares the effectiveness of Generalized Linear Mixed Models (GLMM) and Generalized Estimating Equations (GEE) for beta-distributed longitudinal data. Root Mean Square Error (RMSE) and pseudo R-squared values are used to assess model performance using panel data from 10 provinces between 2018 and 2024 as well as important socioeconomic indicators. With lower RMSE and higher explanatory power across all provincial subsets, the results consistently demonstrate that GLMM performs better than both GEE and generalized linear models (GLM). ANOVA tests verify that modeling methodologies, not data heterogeneity in GRDP or Gini values, are responsible for the differences in model performance. These results demonstrate how well GLMM handles complex data structures and within-subject correlations, providing more accurate and effective estimates in longitudinal beta regression scenarios. The study encourages the use of GLMM for more precise longitudinal analysis in economic and social research and offers insightful information for researchers modeling inequality indices.
Siamese Model-Based Face Verification Using CNN and MobileNetV2 Abd Rahman; Agus Mohamad Soleh; Erfiani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.6996

Abstract

Face verification plays an important role in computer vision, especially in mobile and embedded systems with limited computational capacity. This study proposes a face verification system based on the Siamese Neural Network (SNN) architecture by integrating six embedding models. These models consist of a standard CNN, an L2-normalized CNN, a baseline MobileNetV2, a structurally adjusted MobileNetV2, a pre-trained MobileNetV2, and a fine-tuned MobileNetV2. The dataset includes facial images captured from three webcams and additional samples obtained from the Labeled Faces in the Wild and ImageNet datasets. The experimental procedure includes image preprocessing, construction of balanced positive and negative image pairs, model training, and evaluation using accuracy, precision, recall, F1-score, and AUC. The results show that the pre-trained MobileNetV2 and the standard CNN achieve the highest verification accuracy, reaching 100 percent and 99.998 percent, respectively. Among all models, the structurally adjusted MobileNetV2 presents the best trade-off by combining high accuracy, computational efficiency, and training stability while successfully avoiding overfitting. The real-time implementation involves only the structurally adjusted MobileNetV2 model due to its lightweight structure and consistent performance. This model produces low embedding distances, low latency, and high throughput during CPU-based inference. The performance outperforms GPU execution in one-by-one image processing. The proposed system offers a practical and efficient face verification solution for deployment in identity authentication applications on resource-constrained platforms. These findings support the development of scalable and adaptive biometric security systems that rely on deep learning.
Co-Authors Abd Rahman Agus Mohamad Soleh Alfi Indah Nurrizqi Anang Kurnia Fitrianto, Anwar Ilham Azagi Indahwati Khusnia N. K. Meicheil Yohansa Muftih Alwi Aliu Pardomuan Robinson Sihombing Reni Amelia Titin Yuniarty Yuniarty